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Related Experiment Video

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OrgaQuant: Human Intestinal Organoid Localization and Quantification Using Deep Convolutional Neural Networks.

Timothy Kassis1, Victor Hernandez-Gordillo1, Ronit Langer2

  • 1Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

Scientific Reports
|August 30, 2019
PubMed
Summary

OrgaQuant, a new deep learning tool, accurately quantifies human intestinal organoids in 3D cultures. This automated system overcomes imaging challenges, enabling efficient analysis of organoid growth and morphology.

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Area of Science:

  • * Biomedical Engineering
  • * 3D Cell Culture
  • * Computational Biology

Background:

  • * Organoid cultures serve as advanced in vitro models, closely replicating native tissue cellularity.
  • * Culturing organoids in 3D environments presents imaging challenges due to artifacts, hindering morphological and growth assessments.
  • * Existing automated segmentation techniques are insufficient for localizing and quantifying organoids in 3D cultures.

Purpose of the Study:

  • * To develop an automated method for localizing and quantifying human intestinal organoids in 3D brightfield images.
  • * To address the limitations of current imaging analysis techniques in organoid culture research.

Main Methods:

  • * Development of OrgaQuant, a deep convolutional neural network (CNN) implemented using TensorFlow.
  • * Creation of a unique dataset of manually annotated human intestinal organoid images with bounding boxes.
  • * Training an object detection pipeline for automated analysis without user intervention.

Main Results:

  • * OrgaQuant successfully locates and quantifies the size distribution of human intestinal organoids.
  • * The end-to-end trained neural network requires no parameter tweaking, enabling fully automated analysis.
  • * The system can analyze thousands of images with no user intervention, significantly improving efficiency.

Conclusions:

  • * OrgaQuant provides a reliable and automated solution for analyzing organoid cultures.
  • * The tool overcomes previous imaging and quantification challenges in 3D organoid models.
  • * Publicly releasing the dataset, trained model, and scripts facilitates further research and adoption.